The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
As a typical deep learning method, Deep Belief Network (DBN) and Dropout method are usually used together for pattern recognition in case of lacking training data. Dropout training can avoid the overfitting phenomenon in deep neural network. During the testing stage, the outputs of all neurons in hidden layers are multiplied by a same factor as their actual outputs in the original Dropout method....
Object recognition from depth sensors has recently emerged as a renowned and challenging research topic. The current systems often require large amounts of time to train the models and to classify new data. In this work, we present a novel fast approach for object recognition from 3D data acquired from depth sensors such as Structure or Kinect sensors. We first extract simple but effective frame-level...
Solvents are used in a large number of industries especially in cleaning and cosmetic. Solvents are known to be harmful to human health. Classification of solvent in a product is important to determine the level of hazard that can people faced. In this study, three different solvents, methanol, acetone, and chloroform, are used to obtain binary gas mixtures in a laboratory environment. A gas sensor...
A new change detection method for heterogeneous remote sensing images (i.e. SAR & optics) has been proposed via pixel transformation. It is difficult to directly compare the pixels from heterogeneous images for detecting changes. We propose to transfer the pixels in different images to a common feature space for convenience of comparison. For each pixel in the 1st image, it will be transferred...
As a model of recurrent spiking neural networks, the Liquid State Machine (LSM) offers a powerful brain-inspired computing platform for pattern recognition and machine learning applications. While operated by processing neural spiking activities, the LSM naturally lends itself to an efficient hardware implementation via exploration of typical sparse firing patterns emerged from the recurrent neural...
Passwords are frequently used in data encryption and user authentication. Since people incline to choose meaningful words or numbers as their passwords, lots of passwords are easy to guess. This paper introduces a password guessing method based on Long Short-Term Memory recurrent neural networks. After training our LSTM neural network with 30 million passwords from leaked Rockyou dataset, the generated...
Epileptic seizure source identification involves neurologists combing through a substantial amount of data manually, which sometimes takes weeks per patient. This paper presents a methodology for minimizing the amount of data a neurologist has to analyze to identify the seizure focus. The method keeps the neurologist as the final decision maker and aids in the decision making process. It has to be...
Reconfigurable manufacturing systems are susceptible to disturbances because of the characteristics associated with changeover of machine configuration and functionality. An Artificial Neural Network Driven Decision-Making System can mitigate these disturbances, if applied with extensive knowledge of the manufacturing system. This paper introduces a new concept into the paradigm of agile manufacturing...
Prolonged and sustained warming of the sea, acidification of surface water and rising of sea levels, creates significant habitat losses, resulting in the proliferation and spread of invasive species which immigrate to foreign regions seeking colder climate conditions. This is happening either because their natural habitat does not satisfy the temperature range in which they can survive, or because...
Image classification mainly uses the classifier to classify the extracted image features. In the traditional image feature extraction, it is difficult to set the appropriate feature patterns for the complex images. Simultaneously, the training algorithm of the classifier also affects the accuracy of image classification. In order to solve these problems, the combination of deep belief networks and...
This work evaluates modern convolutional neural networks (CNN) for the task of smoke detection on image data. The networks that were tested are AlexNet, Inception-V3, Inception-V4, ResNet, VGG, and Xception. They all have shown high performance on huge ImageNet dataset, but the possibility of using such CNNs needed to be checked for a very specific task of smoke detection with a high diversity of...
Nearly 10% of all upper limb amputations concern the whole arm. It affects the mobility and reduces the productivity of such a person. These two factors can be restored by using prosthetics. However, the complexity of human arm makes restoring its basic functions quite difficult. When the osseointegration and/or targeted muscle reinnervation (TMR) are not possible, different modalities can be used...
Scaling CMOS integrated circuit technology leads to decrease the chip price and increase processing performance in complex applications with re-configurability. Thus, VLSI architecture is a promising candidate in implementing neural network models nowadays. Backpropagation algorithm is used for training multilayer perceptron with high degree of parallel processing. Parallel computing implementation...
In this paper, a face recognition method based on Convolution Neural Network (CNN) is presented. This network consists of three convolution layers, two pooling layers, two full-connected layers and one Softmax regression layer. Stochastic gradient descent algorithm is used to train the feature extractor and the classifier, which can extract the facial features and classify them automatically. The...
Aiming at the problem that the key water quality parameters in wastewater treatment processing is difficult to detect real-time accurately. An ammonia nitrogen concentration soft measure model based on the artificial neural network(ANN) is proposed in this paper, and utilizing existing data to achieve parameters detection in real-time accurately during the process of wastewater treatment processing...
Due to the limitation in speed and throughput of the traditional Von Neumann architecture, the interest in braininspired neuromorphic systems has been the focus of recent research activities. RRAM device has been extensively used as synapses in neuromorphic systems due to its many advantages including small size and compatibility with CMOS fabrication process. However, the RRAM device suffers from...
Despite recent rapid advances and successful large-scale application of deep Convolutional Neural Networks (CNNs) using image, video, sound, text and time-series data, its adoption within the oil and gas industry in particular have been sparse. In this paper, we initially present an overview of opportunities for deep CNN methods within oil and gas industry, followed by details on a novel development...
Clustering techniques that group samples based on their attribute similarity have been widely used in many fields such as pattern recognition, feature extraction and malicious behavior characterization. Due to its importance, various clustering techniques have been developed with distributed frameworks such as K-means with Hadoop in Apache Mahout for scalable computation. While K-means requires the...
Microcalcifications are an early mammographic indicator of breast cancer. To assist screening radiologists in reading mammograms, machine learning techniques have been developed for the automated detection of microcalcifications. In the last few years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision and medical image analysis applications. A...
Over the last half century, the device community was guided by two quintessential laws that set the roadmap for device work: (1) Moore's law that provided the commercial push to double device count in a cadence of approximately two years and (2) Dennard's scaling laws that provided the physics to do just that. These driving forces slowing down due to power constraints. In fact, the clock frequency...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.